Title: Fixture devices monitoring for machining condition optimisation aided by machine learning

Authors: Felipe Alves de Oliveira Perroni; Ugo Ibusuki; Eduardo de Senzi Zancul; Klaus Schützer; Cláudio Nogueira de Meneses; Thiago Cannabrava de Sousa

Addresses: Polytechnic School of the University of São Paulo, Factory of the Future, University of São Paulo (USP), Av. Prof. Luciano Gualberto, 1380, Butantã, São Paulo – SP, 05508-010, Brazil ' Lean Management 4.0 Research Group, Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangu, Santo André, SP, 09280-560, Brazil ' Polytechnic School of the University of São Paulo, Factory of the Future, University of São Paulo (USP), Av. Prof. Luciano Gualberto, 1380, Butantã, São Paulo – SP, 05508-010, Brazil ' Lean Management 4.0 Research Group, Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangu, Santo André, SP, 09280-560, Brazil ' Centro de Matemática, Computação e Cognição, Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangu, Santo André, SP, 09280-560, Brazil ' Lean Management 4.0 Research Group, Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangu, Santo André, SP, 09280-560, Brazil

Abstract: This paper focuses on applying recent digitisation technologies for machining process improvement based on fixture device monitoring. Industry 4.0 technologies support smart monitoring of manufacturing processes, enabling semi-autonomous tool process parameters adjustment, reducing human-machine interactions, resulting in more accurate process improvements. The paper aims to present the results of a project development and validation of a machining conditioning monitoring system, combining measures conducted directly in the spindle unit and fixture devices. The machining condition monitoring system, aided by a machine learning algorithm, uses vibration data to determine the tool's maximum wear. The project, a collaborative effort by two universities, was designed for practical application and rigorously tested in a real-world operational environment at an automotive company.

Keywords: device monitoring; condition monitoring system; process optimisation; machine learning.

DOI: 10.1504/IJMTM.2025.145949

International Journal of Manufacturing Technology and Management, 2025 Vol.39 No.3/4/5, pp.406 - 422

Received: 23 Oct 2023
Accepted: 03 Jun 2024
Published online: 30 Apr 2025 *

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